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Methods Inf Med 2015; 54(06): 488-499
DOI: 10.3414/ME15-12-0004
Original Articles
Schattauer GmbH

Discussion of “Combining Health Data Uses to Ignite Health System Learning”

S. Denaxas
1   Institute of Health Informatics, University College London, London, United Kingdom
2   Farr Institute of Health Informatics Research, London, United Kingdom
,
C. P. Friedman
3   Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA
,
A. Geissbuhler
4   Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
,
H. Hemingway
1   Institute of Health Informatics, University College London, London, United Kingdom
2   Farr Institute of Health Informatics Research, London, United Kingdom
,
D. Kalra
5   The EuroRec Institute, Gent, Belgium
,
M. Kimura
6   Hamamatsu University, School of Medicine, Department of Medical Informatics, Hamamatsu, Japan
,
K. A. Kuhn
7   Technische Universität München, Chair of Medical Informatics, Munich, Germany
,
H. A. Payne
8   University of Washington, Department of Medicine, UW Medicine IT Services, Seattle, USA
,
F. G. B. de Quiros
9   Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
,
J. C. Wyatt
10   University of Leeds, Leeds Institutes of Health Sciences and Data Analytics, Leeds, United Kingdom
11   University of Southampton, Wessex Institute of Health, Southampton, United Kingdom (from 2016–01–01)
› Author Affiliations
Further Information

Publication History

05 November 2015

Publication Date:
23 January 2018 (online)

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Summary

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper “Combining Health Data Uses to Ignite Health System Learning” written by John D. Ainsworth and Iain E. Buchan [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Ainsworth and Buchan. In subsequent issues the discussion can continue through letters to the editor.

With these comments on the paper “Combining Health Data Uses to Ignite Health System Learning”, written by John D. Ainsworth and Iain E. Buchan [1], the journal seeks to stimulate a broad discussion on new ways for combining data sources for the reuse of health data in order to identify new opportunities for health system learning. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

Keywords

Health data reuse; secondary uses - meaningful use - learning health systems - adaptive health systems - intelligence pipelines - health-care evidence - population health
 

  • References

  • 1 Ainsworth J, Buchan I. Combining Health Data Uses to Ignite Health System Learning. Methods Inf Med 2015; 54 (06) 479-487
    Article in Thieme ConnectPubMedGoogle Scholar
  • 2 Rapsomaniki E, Timmis A, George J, Pujades-Rodriguez M, Shah A, Denaxas S. et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people. The Lancet 2014; 383 9932 1899-1911
    CrossrefPubMedGoogle Scholar
  • 3 Denaxas S, George J, Herrett E, Shah A, Kalra D, Hingorani A. et al. Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER). International Journal of Epidemiology 2012; 41 (06) 1625-1638
    CrossrefPubMedGoogle Scholar
  • 4 Friedman C, Wong A, Blumenthal D. Achieving a nationwide learning health system. Science translational medicine 2010; 2 (57) 57cm29-57cm29.
    PubMedGoogle Scholar
  • 5 Collins F, Varmus H. A new initiative on precision medicine. The New England Journal of Medicine 2015; 372 (09) 793-795
    CrossrefPubMedGoogle Scholar
  • 6 Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine. 2015. 12 (3)
    Google Scholar
  • 7 Denaxas SC, Morley KI. Big biomedical data and cardiovascular disease research: opportunities and challenges. European Heart Journal-Quality of Care and Clinical Outcomes 2015: qcv005
    Google Scholar
  • 8 Angus D. Fusing Randomized Trials With Big Data: The Key to Self-learning Health Care Systems?. JAMA 2015; 314 (08) 767-768
    CrossrefPubMedGoogle Scholar
  • 9 Longhurst C, Harrington R, Shah N. A ’green button’ for using aggregate patient data at the point of care. Health affairs (Project Hope) 2014; 33 (07) 1229-1235
    CrossrefPubMedGoogle Scholar
  • 10 http://www.cdisc.org/therapeutic Accessed Sept 24, 2015)
    PubMed
  • 11 https://www.jami.jp/english/document/doclist.html Accessed Sept 29, 2015)
    PubMed
  • 12 ESF Forward Look Personalised Medicine for the European Citizen http://www.esf.org/publications/forward-looks.html Accessed Mar 05 2015
    PubMed
  • 13 https://www.casym.eu/ Accessed July 13 2015
    PubMed
  • 14 Shiffman RN, Shekelle P, Overhage JM, Slutsky J, Grimshaw J, Deshpande AM. Standardized reporting of clinical practice guidelines: a proposal from the Conference on Guideline Standardization. Ann Intern Med 2003; 139 (06) 493-498
    CrossrefPubMedGoogle Scholar
  • 15 http://www.awmf.org/leitlinien/aktuelle-leitlinien.html last access July 1 2015
    PubMed
  • 16 http://www.cochrane.org/what-is-cochrane- evidence Accessed July 6
    PubMed
  • 17 Moore C, Wisnivesky J, Williams S, McGinn T. Medical errors related to discontinuity of care from an inpatient to an outpatient setting. J Gen Intern Med 2003; 18 (08) 646-651
    CrossrefPubMedGoogle Scholar
  • 18 www.healthit.gov Accessed July 8 2015
    PubMed
  • 19 Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med 2013; 10 (06) 565-576
    CrossrefPubMedGoogle Scholar
  • 20 Paving the Way for Personalized Medicine. FDA, October 2013 http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/Personalized Medicine/UCM372421.pdf Accessed Jan 20 2015
    PubMed
  • 21 Topol EJ, Steinhubl SR, Torkamani A. Digital medical tools and sensors. JAMA 2015; 313 (04) 353-354
    CrossrefPubMedGoogle Scholar
  • 22 Overby CL, Kohane I, Kannry JL, Williams MS, Starren J, Bottinger E, Gottesman O, Denny JC, Weng C, Tarczy-Hornoch P, Hripcsak G. Opportunities for genomic clinical decision support interventions. Genet Med 2013; 15 (10) 817-823
    CrossrefPubMedGoogle Scholar
  • 23 Perakslis ED. Cybersecurity in health care. N Engl J Med 2014; 371 (05) 395-397
    CrossrefPubMedGoogle Scholar
  • 24 Erlich Y, Narayanan A. Routes for breaching and protecting genetic privacy. Nat Rev Genet 2014; 15 (06) 409-421
    CrossrefPubMedGoogle Scholar
  • 25 EU commission Special Eurobarometer 431. DATA PROTECTION REPORT, 2015 http:// ec.europa.eu/public_opinion/archives/ebs/ebs_ 431_en.pdf Accessed July 8, 2015
    PubMed
  • 26 Heeney C, Hawkins N, de Vries J, Boddington P, Kaye J. Assessing the privacy risks of data sharing in genomics. Public Health Genomics 2011; 14 (01) 17-25
    CrossrefPubMedGoogle Scholar
  • 27 Legal and Policy Challenges to Secondary Uses of Information from Electronic Clinical Health Records http://www.academyhealth.org/files/ publications/HIT4AKLegalandPolicy.pdf Accessed July 25 2015
    PubMed
  • 28 http://www.ehr4cr.eu/ Accessed July 6 2015
    PubMed
  • 29 http://www.biomedbridges.eu/ Accessed July 6 2015
    PubMed
  • 30 U.S. Dep’t. of Health, Education and Welfare, Secretary’s Advisory Committee on Automated Personal Data Systems, Records, computers, and the Rights of Citizens (1973) In: Garfinkle S.. Database Nation. The Death of Privacy in the 21st Century. Sebastopol: O’Reilly Media; 2001
    Google Scholar
  • 31 Cimino JJ. The false security of blind dates. Chrononymization’s lack of impact on data privacy of laboratory data. Appl Clin Inform 2012; 3 (04) 392-403
    Article in Thieme ConnectPubMedGoogle Scholar
  • 32 Hammond KW, Helbig ST, Benson CC, Brathwaite-Sketoe BM.. Are electronic medical records trustworthy? Observations on copying, pasting and duplication. AMIA Annu Symp Proc 2003: 269-273
    Google Scholar
  • 33 Siegler EL, Adelman R. Copy and paste: a remediable hazard of electronic health records. Am J Med 2009; 122 (06) 495-496
    CrossrefPubMedGoogle Scholar
  • 34 Kanaan, Susan. Health Data Stewardship: What, Why, Who, How. Oct 12, 2009 http://www.ncvhs.hhs.gov/wp-content/uploads/2014/05/090930lt. pdf Accessed September 2 2015
    PubMed
  • 35 Institute of Medicine (US) Grossmann C, Powers B, McGinnis JM. editors. Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Washington (DC): National Academies Press (US); 2011
    Google Scholar
  • 36 Bergman E, de Feijter J, Frambach J, Godefrooij M, Slootweg I, Stalmeijer R, van der Zwet J. AM last page: A guide to research paradigms relevant to medical education. Acad Med 2012; 87 (04) 545 doi: 10.1097/ACM.0b013e31824fbc8a.
    CrossrefPubMedGoogle Scholar
  • 37 Marsolo K, Spooner SA. Clinical genomics in the world of the electronic health record. Genet Med 2013; 15 (10) 786-791 doi: 10.1038/gim. 2013.88
    CrossrefPubMedGoogle Scholar
  • 38 Marmot M. Health in an unequal world. Lancet 2006; 368: 2081-2094
    CrossrefPubMedGoogle Scholar
  • 39 Behforouz HL, Drain PK, Rhatigan JJ. Rethinking the social history. N Engl J Med 2014; 371 (14) 1277-1279 doi: 10.1056/NEJMp1404846.
    CrossrefPubMedGoogle Scholar
  • 40 Pantell M, Rehkopf D, Jutte D, Syme SL, Balmes J, Adler N. Social isolation: a predictor of mortality comparable to traditional clinical risk factors. Am J Public Health 2013; 103: 2056-2062
    CrossrefPubMedGoogle Scholar
  • 41 Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington (DC): National Academies Press (US); 2015. Jan 08
    Google Scholar
  • 42 Ladyman J, Lambert J, Wiesner K. What is a complex system? Euro Jnl Phil Sci. 2013; 3: 33-67
    PubMedGoogle Scholar
  • 43 Doolittle P. Complex Constructivism: A Theoretical Model of Complexity and Cognition. International Journal of Teaching and Learning in Higher Education 2014; 26 (03) 485-498
    PubMedGoogle Scholar
  • 44 OECD (2009), Applications of Complexity Science for Public Policy: New tools for finding unanticipated consequences and unrealized opportunities. OECD Global Science Forum. Workshop Oc- tober 5-7, Erice, Sicily, 2008. Available at: http://www.oecd.org/sti/sci-tech/43891980.pdf
    PubMed
  • 45 Schwandt T.. Constructivist, interpretivist approaches to human inquiry. (1994). Handbook of qualitative research (pp 118-137) Thousand Oaks, CA, US: Sage Publications, Inc, xii; 643
    Google Scholar
  • 46 John Loder, Laura Bunt and Jeremy C Wyatt. Doctor Know: a Knowledge Commons in Health. London 2013; NESTA www.nesta.org.uk/home1/ assets/features/doctor_know_a_knowledge_ commons_in_health
    PubMed
  • 47 Nichols M, Townsend N, Scarborough P, Rayner M. Trends in age-specific coronary heart disease mortality in the European Union over three decades: 1980-2009. Eur Heart J 2013; 34 (39) 3017-3027 Epub 2013 Jun 25
    CrossrefPubMedGoogle Scholar
  • 48 Peto R, Pike MC, Armitage P, Breslow NE, Cox DR, Howard SV, Mantel N, McPherson K, Peto J, Smith PG. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. I. Introduction and design. Br J Cancer 1976; 34 (06) 585-612
    CrossrefPubMedGoogle Scholar
  • 49 Zwarenstein M, Treweek S, Gagnier JJ, Altman DG, Tunis S, Haynes B, Oxman AD, Moher D. CONSORT group; Pragmatic Trials in Healthcare (Practihc) group. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ 2008; 337: a2390
    CrossrefPubMedGoogle Scholar
  • 50 Byar DP. Why data bases should not replace randomised controlled clinical trials. Biometrics 1980; 36: 337-342
    CrossrefPubMedGoogle Scholar
  • 51 Liu JLY, Wyatt JC.. The case for randomized controlled trials to assess the impact of clinical information systems. JAMIA 2011 http://jamia.bmj. com/content/early/2011/01/26/jamia.2010.010306
    PubMedGoogle Scholar
  • 52 Guyatt G, Sackett D, Taylor DW, Chong J, Roberts R, Pugsley S. Determining optimal therapy - randomized trials in individual patients. N Engl J Med 1986; 314 (14) 889-892
    CrossrefPubMedGoogle Scholar
  • 53 http://www.hra-decisiontools.org.uk/research/
    PubMed
  • 54 van Staa TP, Dyson L, McCann G, Padmanabhan S, Belatri R, Goldacre B, Cassell J, Pirmohamed M, Torgerson D, Ronaldson S, Adamson J, Taweel A, Delaney B, Mahmood S, Baracaia S, Round T, Fox R, Hunter T, Gulliford M, Smeeth L. The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. Health Technol Assess 2014; 18 (43) 1-146 doi: 10.3310/hta18430.
    CrossrefPubMedGoogle Scholar
  • 55 Adam D.I Kramer, Jamie E Guillory, Jeffrey T.. Hancock. Experimental evidence of massive-scale emotional contagion through social networks. PNAS 2013; 111 (24) 8788-8790 doi: 10.1073/ pnas.1320040111
    PubMedGoogle Scholar
  • 56 Julous SA, Mullee MA. Confounding and Simpson’s Paradox. BMJ 1994; 309: 1480-1481
    CrossrefPubMedGoogle Scholar